Phylogenetic Methods and the Prehistory of LanguagesPeter Forster, Colin Renfrew McDonald Institute for Archaeological Research, 2006 - 198 Seiten Evolutionary ('phylogenetic') trees were first used to infer lost histories nearly two centuries ago by manuscript scholars reconstructing original texts. Today, computer methods are enabling phylogenetic trees to transform genetics, historical linguistics and even the archaeological study of artefact shapes and styles. But which phylogenetic methods are best suited to retracing the evolution of languages? And which types of language data are most informative about deep prehistory? In this book, leading specialists engage with these key questions. Essential reading for linguists, geneticists and archaeologists, these studies demonstrate how phylogenetic tools are illuminating previously intractable questions about language prehistory. This innovative volume arose from a conference of linguists, geneticists and archaeologists held at Cambridge in 2004. |
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Seite 78
... assume that different characters have the same stochastic substitution matrices on any given edge , nor do we assume that these substitu- tion matrices cannot change as we move across the tree . In this sense the model is highly ...
... assume that different characters have the same stochastic substitution matrices on any given edge , nor do we assume that these substitu- tion matrices cannot change as we move across the tree . In this sense the model is highly ...
Seite 82
... assumes that all char- acters are kept , and none are removed from the data set . What about the traditional approach in ... Assume further that there are constants 0 < a < b < 1 such that a ≤ Pe , ( n , n ′ ) ≤ b for all edges e and ...
... assumes that all char- acters are kept , and none are removed from the data set . What about the traditional approach in ... Assume further that there are constants 0 < a < b < 1 such that a ≤ Pe , ( n , n ′ ) ≤ b for all edges e and ...
Seite 181
... Assume that some proportion of the k sites k , are independent of one another . Then , the true likelihood of the data is given by the product of the individual likelihoods of these k , sites : L1 = P ( M ; 1 | QT ) What is the ...
... Assume that some proportion of the k sites k , are independent of one another . Then , the true likelihood of the data is given by the product of the individual likelihoods of these k , sites : L1 = P ( M ; 1 | QT ) What is the ...
Inhalt
CLARE J HOLDEN RUSSELL D GRAY | 19 |
Bantu Classification Bantu Trees and Phylogenetic Methods | 43 |
Chapter 6 | 67 |
Urheberrecht | |
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Albanian algorithms Anatolian Archaeological assumptions Bantu languages Bantu trees Bastin Bayesian binary Biology borrowing branch lengths Cambridge Chapter clade cladistics classification coded cognate cognate class cognate sets comparative computational correspondences data set data-cognate dating dialects distribution divergence Dyen East Bantu edge English estimates evidence evolutionary example Figure Forster genetic Germanic glottochronology Gray & Atkinson Greek guages Historical Linguistics Hittite Holden homoplasy Indo-European languages Indo-Iranian inference innovations islands language data language evolution language family lexical evolution lexical replacement lexicostatistics likelihood Malagasy Markov matrix maximum parsimony McDonald Institute McMahon meaning Molecular morphological Mycenaean Neighbor-Net Nichols nodes Pagel parameters phonetic phonological characters phylogenetic methods phylogenetic trees phylogeny posterior probability probability problem Proto-Indo-European rates of lexical reconstruction relationships Renfrew reticulations root semantic slot similar split splits graph statistical subgroup Swadesh Swadesh list telic tion Tocharian verbs vocabulary Warnow word lists zone